




'''simple docstring'''


import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

UpperCAmelCase_					=   {
    "vocab_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
    },
    "emoji_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
    },
}

UpperCAmelCase_					=   {
    "abeja/gpt-neox-japanese-2.7b": 20_48,
}


def     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				Union[str, Any]      ):
  with open(a_					,       'r'					,       encoding='utf-8'      ) as f:
    __a								=       json.loads(f.read()      )

  __a								=       collections.OrderedDict()
  __a								=       collections.OrderedDict()
  __a								=       collections.OrderedDict()
  with open(a_					,       'r'					,       encoding='utf-8'      ) as f:
    __a								=       f.readlines()
  __a								=       [[t.rstrip('\n'      )] if (t == ',' or ',' not in t) else t.rstrip('\n'      ).split(','      ) for t in token]
  for idx, b in enumerate(a_      ):
    __a								=       b
    __a								=       idx
    for wd in b:
      __a								=       idx

  return vocab, raw_vocab, ids_to_tokens, emoji





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   ["""input_ids""", """attention_mask"""]







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|startoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     Any:
     super().__init__(
         unk_token=UpperCamelCase   ,				pad_token=UpperCamelCase   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				do_clean_text=UpperCamelCase   ,				**UpperCamelCase   ,				)
     if not os.path.isfile(UpperCamelCase   ):
       raise ValueError(
           f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
           ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'   )
     if not os.path.isfile(UpperCamelCase   ):
       raise ValueError(
           f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
           ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'   )
     __a								=       do_clean_text
     __a						,							__a						,							__a						,							__a								=       load_vocab_and_emoji(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       SubWordJapaneseTokenizer(
         vocab=self.vocab   ,				ids_to_tokens=self.ids_to_tokens   ,				emoji=self.emoji   )







   @property
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
     return len(self.raw_vocab   )







   def 		UpperCamelCase__      (						self   )					->     int:
     return dict(self.raw_vocab   ,				**self.added_tokens_encoder   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     return self.subword_tokenizer.tokenize(UpperCamelCase   ,				clean=self.do_clean_text   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     return self.vocab.get(UpperCamelCase   ,				self.vocab.get(self.unk_token   )   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Tuple:
     return self.subword_tokenizer.convert_id_to_token(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     __a								=       ''.join(UpperCamelCase   ).strip()
     return out_string







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[int]:
     __a								=       []
     for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(UpperCamelCase   ,				add_special_tokens=UpperCamelCase   ) + [self.eos_token_id]   )

     if len(UpperCamelCase   ) > self.model_max_length:
       __a								=       input_ids[-self.model_max_length :]
     return input_ids







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     Tuple[str]:
     __a								=       0
     if os.path.isdir(UpperCamelCase   ):
       __a								=       os.path.join(
           UpperCamelCase   ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
       __a								=       os.path.join(
           UpperCamelCase   ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']   )
     else:
       __a								=       (
           (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
       )
       __a								=       (
           (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
       )
     with open(UpperCamelCase   ,				'w'   ,				encoding='utf-8'   ) as writer:
       for token_index, token in self.ids_to_tokens.items():
         if index != token_index:
           logger.warning(
               f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
               ' Please check that the vocabulary is not corrupted!'   )
           __a								=       token_index
         writer.write(','.join(UpperCamelCase   ) + '\n'   )
         index += 1
     with open(UpperCamelCase   ,				'w'   ,				encoding='utf-8'   ) as writer:
       json.dump(self.emoji   ,				UpperCamelCase   )
     return vocab_file, emoji_file





class  __lowercase    (	__magic_name__			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Union[str, Any]:
     __a								=       vocab  # same as swe
     __a								=       ids_to_tokens  # same as bpe
     __a								=       emoji
     __a								=       np.max([len(UpperCamelCase   ) for w in self.vocab.keys()]   )
     __a								=       re.compile(R'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'   )
     __a								=       re.compile(R'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'   )
     __a								=       re.compile(R'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'   )
     __a								=       re.compile(
         R'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'   )
     __a								=       re.compile(
         R'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'   )
     __a								=       re.compile(
         R'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'   )
     __a								=       '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
     __a								=       '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
     __a								=       str.maketrans({k: '<BLOCK>' for k in keisen + blocks}   )







   def __len__(						self   )					->     Union[str, Any]:
     return len(self.ids_to_tokens   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[int]:
     __a								=       self.content_repattera.sub('<URL>'   ,				UpperCamelCase   )
     __a								=       self.content_repattera.sub('<EMAIL>'   ,				UpperCamelCase   )
     __a								=       self.content_repattera.sub('<TEL>'   ,				UpperCamelCase   )
     __a								=       self.content_repattera.sub('<DATE>'   ,				UpperCamelCase   )
     __a								=       self.content_repattera.sub('<DATE>'   ,				UpperCamelCase   )
     __a								=       self.content_repattera.sub('<PRICE>'   ,				UpperCamelCase   )
     __a								=       content.translate(self.content_transa   )
     while "<BLOCK><BLOCK>" in content:
       __a								=       content.replace('<BLOCK><BLOCK>'   ,				'<BLOCK>'   )
     return content







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=False   )					->     Any:
     __a								=       text.replace(' '   ,				'<SP>'   )
     __a								=       text.replace('　'   ,				'<SP>'   )
     __a								=       text.replace('\r\n'   ,				'<BR>'   )
     __a								=       text.replace('\n'   ,				'<BR>'   )
     __a								=       text.replace('\r'   ,				'<BR>'   )
     __a								=       text.replace('\t'   ,				'<TAB>'   )
     __a								=       text.replace('—'   ,				'ー'   )
     __a								=       text.replace('−'   ,				'ー'   )
     for k, v in self.emoji["emoji"].items():
       if k in text:
         __a								=       text.replace(UpperCamelCase   ,				UpperCamelCase   )
     if clean:
       __a								=       self.clean_text(UpperCamelCase   )

     def check_simbol(UpperCamelCase   ):
       __a								=       x.encode()
       if len(UpperCamelCase   ) == 1 and len(UpperCamelCase   ) == 2:
         __a								=       (int(e[0]   ) << 8) + int(e[1]   )
         if (
             (c >= 0xC_2_A_1 and c <= 0xC_2_B_F)
             or (c >= 0xC_7_8_0 and c <= 0xC_7_8_3)
             or (c >= 0xC_A_B_9 and c <= 0xC_B_B_F)
             or (c >= 0xC_C_8_0 and c <= 0xC_D_A_2)
         ):
           return True
       return False

     def checkuae(UpperCamelCase   ):
       __a								=       x.encode()
       if len(UpperCamelCase   ) == 1 and len(UpperCamelCase   ) == 3:
         __a								=       (int(e[0]   ) << 16) + (int(e[1]   ) << 8) + int(e[2]   )
         if c >= 0xE_2_8_0_8_0 and c <= 0xE_2_B_0_7_F:
           return True
       return False

     __a								=       0
     __a								=       []
     while pos < len(UpperCamelCase   ):
       __a								=       min(len(UpperCamelCase   )   ,				pos + self.maxlen + 1   ) if text[pos] == '<' else pos + 3
       __a								=       []  # (token_id, token, pos)
       for e in range(UpperCamelCase   ,				UpperCamelCase   ,				-1   ):
         __a								=       text[pos:e]
         if wd in self.vocab:
           if wd[0] == "<" and len(UpperCamelCase   ) > 2:
             __a								=       [(self.vocab[wd], wd, e)]
             break
           else:
             candidates.append((self.vocab[wd], wd, e)   )
       if len(UpperCamelCase   ) > 0:
         # the smallest token_id is adopted
         __a						,							__a						,							__a								=       sorted(UpperCamelCase   ,				key=lambda UpperCamelCase   : x[0]   )[0]
         result.append(UpperCamelCase   )
         __a								=       e
       else:
         __a								=       pos + 1
         __a								=       text[pos:end]
         if check_simbol(UpperCamelCase   ):
           result.append('<KIGOU>'   )
         elif checkuae(UpperCamelCase   ):
           result.append('<U2000U2BFF>'   )
         else:
           for i in wd.encode('utf-8'   ):
             result.append('<|byte%d|>' % i   )
         __a								=       end
     return result







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase="\n"   )					->     Tuple:
     __a								=       []
     __a								=       []
     __a								=       self.ids_to_tokens[index][0]
     if word[:6] == "<|byte" and word[-2:] == "|>":
       byte_tokens.append(int(word[6:-2]   )   )
     else:
       if len(UpperCamelCase   ) > 0:
         words.append(bytearray(UpperCamelCase   ).decode('utf-8'   ,				errors='replace'   )   )
         __a								=       []
       if word[:7] == "<|emoji" and word[-2:] == "|>":
         words.append(self.emoji['emoji_inv'][word]   )
       elif word == "<SP>":
         words.append(' '   )
       elif word == "<BR>":
         words.append(UpperCamelCase   )
       elif word == "<TAB>":
         words.append('\t'   )
       elif word == "<BLOCK>":
         words.append('▀'   )
       elif word == "<KIGOU>":
         words.append('ǀ'   )
       elif word == "<U2000U2BFF>":
         words.append('‖'   )
       else:
         words.append(UpperCamelCase   )
     if len(UpperCamelCase   ) > 0:
       words.append(bytearray(UpperCamelCase   ).decode('utf-8'   ,				errors='replace'   )   )
     __a								=       ''.join(UpperCamelCase   )
     return text









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



